🚀 Garbage Detection for Water Cleaning Robot using Tuned YOLOv8 Model Momentum MU¶

Welcome to our project!¶

This work is part of Momentum MU Team at Mansoura National University, focusing on developing an intelligent Water Cleaning Robot. 🌊🤖

In this notebook, we demonstrate how we trained and tuned a YOLOv8 model for garbage detection to be deployed directly on our robot.
The robot will navigate and identify waste objects floating on water surfaces, enabling efficient and automated cleaning.

📸 Momentum MU Cleaning Robot¶

image.png

🛠️ Project Highlights:¶

  • Model: YOLOv8 (custom-tuned for garbage detection)
  • Platform: ESP32 WROOM + Robotic Unit
  • Purpose: Detect and localize garbage items to assist in water cleaning.
  • Team: Momentum MU Team - Mansoura University
  • Robot Name: Water Cleaning Robot
In [1]:
!ls /kaggle/input/garbage-data

data.yaml  README.dataset.txt  README.roboflow.txt  test  train  valid
In [2]:
!pip install ultralytics

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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pylibcugraph-cu12 24.12.0 requires pylibraft-cu12==24.12.*, but you have pylibraft-cu12 25.2.0 which is incompatible.
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Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 ultralytics-8.3.117 ultralytics-thop-2.0.14
In [4]:
!cp -r /kaggle/input/garbage-data /kaggle/working/garbage-data

Explor The Data¶

In [30]:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Load your image
img = mpimg.imread('/kaggle/working/garbage-data/train/images/000545081W_jpg.rf.feca471c9b6a6e19bb78d8a8dd509298.jpg')

# Plot the image
plt.figure(figsize=(8, 6))  # you can change the size if you want
plt.imshow(img)
plt.axis('off')  # Hide axis
plt.title('Sample Garbage Image', fontsize=16)
plt.show()
In [32]:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Load your five images
img1 = mpimg.imread('/kaggle/working/garbage-data/train/images/000568727W_jpg.rf.10438490dc09a5e1da5dad923824a637.jpg')
img2 = mpimg.imread('/kaggle/working/garbage-data/valid/images/004633f2-679f-11e5-b0e3-40f2e96c8ad8_jpg.rf.c7fb6b39d01d029e82c697391873876d.jpg')
img3 = mpimg.imread('/kaggle/working/garbage-data/valid/images/000273202W_jpg.rf.2a0c7f191a80ad024b90ce66a2cb69c0.jpg')
img4 = mpimg.imread('/kaggle/working/garbage-data/train/images/05-_RESAH_jpg.rf.88fa789ca32b7e2debd198713f1debc3.jpg')
img5 = mpimg.imread('/kaggle/working/garbage-data/train/images/-de-s-enfoncer-tres-loin-pour-tomber-sur-des-immondices-photos-rl-15403880372_jpg.rf.7d0befa323c2f0ff9680687398f6f68a.jpg')

# Create a figure
fig, axes = plt.subplots(1, 5, figsize=(20, 5))  # 1 row, 5 columns

# List of images
images = [img1, img2, img3, img4, img5]
titles = ['Detection 1', 'Detection 2', 'Detection 3', 'Detection 4', 'Detection 5']

# Plot each image
for i in range(5):
    axes[i].imshow(images[i])
    axes[i].axis('off')
    axes[i].set_title(titles[i], fontsize=12)

plt.tight_layout()
plt.show()

Update the ymal file¶

In [6]:
import os
dataset_path = '/kaggle/working/garbage-data'
In [7]:
with open(os.path.join(dataset_path, 'data.yaml'), 'r') as f:
    yaml_content = f.read()
yaml_content = yaml_content.replace('train: train', f'train: {dataset_path}/train')
yaml_content = yaml_content.replace('val: valid', f'val: {dataset_path}/valid')
yaml_content = yaml_content.replace('test: test', f'test: {dataset_path}/test')
with open(os.path.join(dataset_path, 'data.yaml'), 'w') as f:
    f.write(yaml_content)

Train the model using the updated data.yaml¶

In [8]:
!yolo task=detect mode=train model=yolov8n.pt data=/kaggle/working/garbage-data/data.yaml epochs=50 imgsz=640 batch=16 name=yolov8n_garbage

Creating new Ultralytics Settings v0.0.6 file ✅ 
View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'
Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.
Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt to 'yolov8n.pt'...
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Ultralytics 8.3.117 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla P100-PCIE-16GB, 16269MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=/kaggle/working/garbage-data/data.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=yolov8n_garbage, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/yolov8n_garbage
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Overriding model.yaml nc=80 with nc=1

                   from  n    params  module                                       arguments                     
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  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    751507  ultralytics.nn.modules.head.Detect           [1, [64, 128, 256]]           
Model summary: 129 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs

Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...
100%|███████████████████████████████████████| 5.35M/5.35M [00:00<00:00, 117MB/s]
AMP: checks passed ✅
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 1707.4±449.5 MB/s, size: 91.4 KB)
train: Scanning /kaggle/working/garbage-data/train/labels... 3669 images, 210 ba
train: New cache created: /kaggle/working/garbage-data/train/labels.cache
WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 6, len(boxes) = 3933. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 874.5±570.1 MB/s, size: 60.0 KB)
val: Scanning /kaggle/working/garbage-data/valid/labels... 260 images, 8 backgro
val: New cache created: /kaggle/working/garbage-data/valid/labels.cache
WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 2, len(boxes) = 271. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.
Plotting labels to runs/detect/yolov8n_garbage/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.002, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 4 dataloader workers
Logging results to runs/detect/yolov8n_garbage
Starting training for 50 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/50      2.21G      1.357      1.833      1.802         10        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.638      0.557      0.572      0.247

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/50      2.46G      1.433      1.516       1.84         10        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.667      0.579      0.593      0.282

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/50      2.46G      1.432      1.442      1.834         11        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.675      0.491      0.541       0.21

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/50      2.46G      1.395      1.366      1.806         15        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.743      0.572      0.656      0.308

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/50      2.46G      1.363      1.322       1.78         17        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.718      0.648      0.686      0.368

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/50      2.46G      1.307      1.256      1.717         14        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.841      0.738      0.766      0.394

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/50      2.46G      1.278       1.21      1.701         15        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.744      0.742      0.771      0.421

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/50      2.46G      1.235       1.19      1.674         10        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271       0.79       0.69      0.756      0.395

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/50      2.46G      1.191      1.134      1.634         14        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.749      0.742      0.748      0.391

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/50      2.46G      1.174      1.112      1.621         10        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271        0.8      0.798      0.794      0.452

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      11/50      2.46G      1.149      1.109      1.597         12        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.772      0.763      0.779      0.438

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      12/50      2.46G      1.129      1.077      1.584          8        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.838      0.768      0.808      0.463

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      13/50      2.46G      1.116      1.044      1.573          8        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.828      0.763      0.808      0.477

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      14/50      2.47G      1.091      1.029      1.556         16        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.818      0.723      0.796       0.44

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      15/50      2.47G      1.083      1.014      1.539         11        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271       0.84       0.79      0.823      0.477

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      16/50      2.47G      1.086      1.002      1.551         15        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271       0.82       0.76       0.81      0.474

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      17/50      2.47G      1.042     0.9706      1.518         16        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.811      0.793      0.826      0.487

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      18/50      2.47G      1.065     0.9838      1.528         16        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271       0.83      0.809      0.818      0.467

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      19/50      2.47G      1.054     0.9788      1.521         14        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.803      0.742      0.811      0.456

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      20/50      2.47G      1.019     0.9414      1.501         17        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.862      0.793      0.842      0.482

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      21/50      2.47G      1.008     0.9389      1.497         11        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271       0.82      0.808      0.839      0.485

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      22/50      2.47G     0.9825     0.9203      1.474          8        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.885      0.786      0.844      0.507

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      23/50      2.47G     0.9798      0.901      1.471         15        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271        0.8      0.723      0.787      0.435

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      24/50      2.47G     0.9749        0.9      1.462         13        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.878      0.771      0.837      0.515

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      25/50      2.47G     0.9739      0.894      1.466         15        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.847      0.817      0.841      0.499

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      26/50      2.47G     0.9593     0.8978      1.453         14        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.853      0.764      0.842      0.474

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      27/50      2.47G      0.952     0.8619      1.436         20        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.893       0.79       0.84      0.502

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      28/50      2.47G     0.9557     0.8709      1.449         18        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271       0.81      0.816      0.821      0.498

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      29/50      2.47G     0.9431     0.8578      1.439         14        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.837      0.786      0.843      0.505

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      30/50      2.47G     0.9246     0.8349      1.422         13        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.839      0.723      0.787      0.461

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      31/50      2.47G     0.9262     0.8457      1.418         10        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.861      0.799      0.837       0.51

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      32/50      2.47G     0.9287     0.8327      1.431          9        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.866      0.838      0.872      0.537

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      33/50      2.47G     0.9043     0.8222      1.412          9        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.852      0.779      0.844      0.513

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      34/50      2.47G     0.9014     0.8255      1.398         23        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.842      0.824      0.851      0.531

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      35/50      2.47G     0.8998     0.8096      1.402         11        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.875      0.812      0.848      0.517

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      36/50      2.47G     0.8689     0.7895      1.389         13        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271        0.9      0.829      0.887      0.547

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      37/50      2.47G     0.9004      0.805      1.402         13        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.865      0.819      0.858      0.528

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      38/50      2.47G     0.8742     0.7788      1.383         11        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271       0.87      0.818      0.871      0.547

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      39/50      2.47G     0.8651     0.7676      1.376         14        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271        0.9      0.838      0.871       0.54

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      40/50      2.47G     0.8713     0.7776       1.38         12        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.895      0.818      0.872      0.542
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      41/50      2.47G     0.8416     0.6736      1.523          5        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.883      0.808      0.853      0.543

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      42/50      2.47G     0.7991     0.6192      1.478          6        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.881       0.82      0.857       0.54

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      43/50      2.47G     0.7842     0.5976      1.456          5        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.894      0.827      0.868      0.562

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      44/50      2.47G     0.7556     0.5787      1.432          4        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.872      0.827      0.872      0.558

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      45/50      2.47G     0.7526     0.5697      1.417          5        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271       0.89      0.827       0.87      0.554

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      46/50      2.47G     0.7438     0.5634      1.413          5        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.901      0.838      0.881       0.56

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      47/50      2.47G     0.7229     0.5428      1.392          4        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.866      0.841      0.869       0.55

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      48/50      2.47G     0.7148     0.5397       1.39          5        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271        0.9      0.828      0.863      0.564

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      49/50      2.47G      0.709     0.5305       1.38          8        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271       0.88       0.83      0.865      0.565

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      50/50      2.47G     0.7024     0.5251      1.385          7        640: 1
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.894      0.843      0.862      0.566

50 epochs completed in 0.595 hours.
Optimizer stripped from runs/detect/yolov8n_garbage/weights/last.pt, 6.2MB
Optimizer stripped from runs/detect/yolov8n_garbage/weights/best.pt, 6.2MB

Validating runs/detect/yolov8n_garbage/weights/best.pt...
Ultralytics 8.3.117 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla P100-PCIE-16GB, 16269MiB)
Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.894      0.842      0.862      0.566
/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less
  xa[xa < 0] = -1
/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less
  xa[xa < 0] = -1
Speed: 0.2ms preprocess, 1.9ms inference, 0.0ms loss, 1.9ms postprocess per image
Results saved to runs/detect/yolov8n_garbage
💡 Learn more at https://docs.ultralytics.com/modes/train
In [12]:
!ls //kaggle/working/runs/detect/yolov8n_garbage/weights

best.pt  last.pt
In [13]:
!yolo task=detect mode=val model=/kaggle/working/runs/detect/yolov8n_garbage/weights/best.pt data=/kaggle/working/garbage-data/data.yaml

Ultralytics 8.3.117 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla P100-PCIE-16GB, 16269MiB)
Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 1567.5±919.9 MB/s, size: 80.2 KB)
val: Scanning /kaggle/working/garbage-data/valid/labels.cache... 260 images, 8 b
WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 2, len(boxes) = 271. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.
                 Class     Images  Instances      Box(P          R      mAP50  m
                   all        260        271      0.894      0.842      0.862      0.567
/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less
  xa[xa < 0] = -1
/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less
  xa[xa < 0] = -1
Speed: 1.2ms preprocess, 3.0ms inference, 0.0ms loss, 1.8ms postprocess per image
Results saved to runs/detect/val
💡 Learn more at https://docs.ultralytics.com/modes/val
In [14]:
!yolo task=detect mode=predict model=/kaggle/working/runs/detect/yolov8n_garbage/weights/best.pt source=/kaggle/working/garbage-data/test/images conf=0.25 save=True

Ultralytics 8.3.117 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla P100-PCIE-16GB, 16269MiB)
Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs

image 1/258 /kaggle/working/garbage-data/test/images/000144244W_jpg.rf.d1dc6442492ffbc7bcce5f0412bfea66.jpg: 640x640 3 garbages, 6.2ms
image 2/258 /kaggle/working/garbage-data/test/images/000839979_896x598_c_jpg.rf.04eba3aa5dbc551810d5b28ac9c38c53.jpg: 640x640 1 garbage, 6.0ms
image 3/258 /kaggle/working/garbage-data/test/images/02f330f2-9439-11e5-982d-40f2e96c8ad8_jpg.rf.af9f304e5a78fc6f84d268edef376fe3.jpg: 640x640 1 garbage, 5.4ms
image 4/258 /kaggle/working/garbage-data/test/images/07ff75e6-6799-11e5-8dd0-40f2e96c8ad8_jpg.rf.6e2fc221cd02c7e37fec6ca9af25d9f5.jpg: 640x640 1 garbage, 5.3ms
image 5/258 /kaggle/working/garbage-data/test/images/0_The-rubbish-left-behind-in-Sefton-ParkPic-Andrew-Teebay_jpg.rf.e04ea6eb8b005b7c768ef3abda9389b8.jpg: 640x640 1 garbage, 5.3ms
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image 212/258 /kaggle/working/garbage-data/test/images/images74_jpg.rf.452fb1255d62fd113d9c4d023a5f590d.jpg: 640x640 1 garbage, 6.0ms
image 213/258 /kaggle/working/garbage-data/test/images/images75_jpg.rf.cdd4f70eeedafd37f0c406a4818e79b9.jpg: 640x640 1 garbage, 5.3ms
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image 216/258 /kaggle/working/garbage-data/test/images/images95_jpg.rf.344b7c55cb5e5ce253399c74c3ad4569.jpg: 640x640 1 garbage, 6.0ms
image 217/258 /kaggle/working/garbage-data/test/images/images96_jpg.rf.a42e1a1f5112e1d5310ba5eb306a4572.jpg: 640x640 1 garbage, 5.6ms
image 218/258 /kaggle/working/garbage-data/test/images/images_jpg.rf.0c69276365227c03e1b171a4340278dd.jpg: 640x640 1 garbage, 5.4ms
image 219/258 /kaggle/working/garbage-data/test/images/imago0114112886h-scaled_jpg.rf.88a40dbe1bfb068dd13f80cef7ae77f6.jpg: 640x640 1 garbage, 5.3ms
image 220/258 /kaggle/working/garbage-data/test/images/img-9980_jpg.rf.63bcf9a43ac3e5a0a6e4f1b7bdbfe9ff.jpg: 640x640 2 garbages, 5.2ms
image 221/258 /kaggle/working/garbage-data/test/images/img_800x5332010_12_25_23_16_00_245443_jpg.rf.f228230c7c651a3198ad220f755be62e.jpg: 640x640 1 garbage, 5.6ms
image 222/258 /kaggle/working/garbage-data/test/images/int-42278_jpg.rf.4b617e68da8e7c6e384fb0f30c359939.jpg: 640x640 1 garbage, 5.4ms
image 223/258 /kaggle/working/garbage-data/test/images/landfill-with-many-broken-objects-picture-id965404668_jpg.rf.b71bc83730c7726776e868be1822f9d5.jpg: 640x640 1 garbage, 5.3ms
image 224/258 /kaggle/working/garbage-data/test/images/large-pile-of-garbage-on-street-picture-id967982706_jpg.rf.93f87516c99c65b064c70e6bb9383d6e.jpg: 640x640 1 garbage, 5.3ms
image 225/258 /kaggle/working/garbage-data/test/images/lavagem_de_ruas_1-825x510_jpg.rf.cc0b9c18ac7babc56620a21fbaa43f78.jpg: 640x640 (no detections), 5.2ms
image 226/258 /kaggle/working/garbage-data/test/images/les-ordures-dans-la-foret-constituees-de-bouteilles-en-plastique-et-sacs-des-sur-le-cote-du-sentier-159247704_jpg.rf.005298cc8adec0a1b31a30bb50569500.jpg: 640x640 1 garbage, 5.4ms
image 227/258 /kaggle/working/garbage-data/test/images/lixo-da-poluiC3A7C3A3o-florestal-deixado-na-floresta-perto-estrada-176148111_jpg.rf.8c505327ad8a12895ff11924263589a3.jpg: 640x640 3 garbages, 5.3ms
image 228/258 /kaggle/working/garbage-data/test/images/lixoSantoTirso-770x439_c_jpg.rf.f930a1557d15b7dcd2aa09a8d5ef52d4.jpg: 640x640 1 garbage, 5.2ms
image 229/258 /kaggle/working/garbage-data/test/images/lixoforacaixote-e1547205506682-770x439_c_jpg.rf.bc748288769942e93a385becb3ebea74.jpg: 640x640 2 garbages, 5.9ms
image 230/258 /kaggle/working/garbage-data/test/images/maxresdefault_jpg.rf.5126159eb6fd1b9a02cc85f5181917f5.jpg: 640x640 2 garbages, 5.4ms
image 231/258 /kaggle/working/garbage-data/test/images/mull-1_1558549054_jpg.rf.89fa1c5baa5f9df881a2ce3408a7dc88.jpg: 640x640 1 garbage, 6.0ms
image 232/258 /kaggle/working/garbage-data/test/images/neues-konzept-fuer-stadtfuehrungen-soll-die-stadt-sauberer-werden-archivbild_jpg.rf.bbb5cd29de11a0fe6cb5231c27d32089.jpg: 640x640 1 garbage, 5.3ms
image 233/258 /kaggle/working/garbage-data/test/images/orduresmarseilleune2_jpg.rf.7c1e084c9055b27330d2b0c4386b4a90.jpg: 640x640 2 garbages, 5.5ms
image 234/258 /kaggle/working/garbage-data/test/images/p17-brasor-pollution-a-20180603_jpg.rf.d10945c4963311fa22b68ebdc8060138.jpg: 640x640 1 garbage, 5.5ms
image 235/258 /kaggle/working/garbage-data/test/images/photo-43_jpg.rf.43c9ccc21acb46e57230a7b3eda5e6ae.jpg: 640x640 1 garbage, 5.5ms
image 236/258 /kaggle/working/garbage-data/test/images/pile-black-garbage-bag-and-bucket-roadside-in-the-city-picture-id936363766_jpg.rf.e1c28d83542c9730f6f4a2cf5d658630.jpg: 640x640 1 garbage, 5.3ms
image 237/258 /kaggle/working/garbage-data/test/images/pile-black-garbage-roadside-and-rain-drops-on-the-bag-in-the-city-picture-id960699652_jpg.rf.f68379437119b52531705bf17371cd12.jpg: 640x640 1 garbage, 5.5ms
image 238/258 /kaggle/working/garbage-data/test/images/pile-black-garbage-roadside-and-rain-drops-on-the-bag-in-the-city-picture-id960702408_jpg.rf.9b54f1759e4447c1a9e6e42260d212ec.jpg: 640x640 1 garbage, 5.2ms
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image 240/258 /kaggle/working/garbage-data/test/images/pollution-forC3AAts_jpg.rf.1851be03f2a79b8581a6e811fa3ebab0.jpg: 640x640 1 garbage, 5.3ms
image 241/258 /kaggle/working/garbage-data/test/images/recyclable-waste-picture-id844862542_jpg.rf.8825df04b1b20ad6e65f37f99448f939.jpg: 640x640 1 garbage, 5.6ms
image 242/258 /kaggle/working/garbage-data/test/images/rifiuti-16_jpg.rf.8649c4bcf5b03cdc9fd6c8ce03877508.jpg: 640x640 1 garbage, 6.2ms
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image 244/258 /kaggle/working/garbage-data/test/images/rifiuti-reggio-ripresi-da-google_jpeg.rf.08f57475fe13d6b04d0e98ffc433a82b.jpg: 640x640 1 garbage, 5.5ms
image 245/258 /kaggle/working/garbage-data/test/images/rifiuti-roma-emergenza-immondizia_jpeg.rf.a2e7c05f5c36fb0d9ebf6fd037e96327.jpg: 640x640 1 garbage, 5.7ms
image 246/258 /kaggle/working/garbage-data/test/images/rubbish0100-1-390x285_jpg.rf.2260afc7aa554f800f51436b2be0438a.jpg: 640x640 1 garbage, 6.5ms
image 247/258 /kaggle/working/garbage-data/test/images/segnalazione_jpg.rf.cbf5bcf1285ecff1f98fc1a96c2f39d5.jpg: 640x640 1 garbage, 5.4ms
image 248/258 /kaggle/working/garbage-data/test/images/sous-les-poubelles_jpg.rf.b41787ac31894f89cb1f0f78b2602380.jpg: 640x640 1 garbage, 5.4ms
image 249/258 /kaggle/working/garbage-data/test/images/spazza-vibo1-696x607-1-1-1280x720_jpg.rf.8d8cd2f864680a3c4f52c6fd64aeb4b1.jpg: 640x640 1 garbage, 5.3ms
image 250/258 /kaggle/working/garbage-data/test/images/spazzatura_png.rf.3da74870d8b09675d64624ce8dbdaf92.jpg: 640x640 1 garbage, 5.9ms
image 251/258 /kaggle/working/garbage-data/test/images/sqJu3jr_jpg.rf.6134a97bdfb40b33f5e51c6a1ab4b4c3.jpg: 640x640 1 garbage, 5.3ms
image 252/258 /kaggle/working/garbage-data/test/images/telechargement-1-_jpg.rf.5f221e04a8dffb0e307a4c9077b747f9.jpg: 640x640 1 garbage, 5.4ms
image 253/258 /kaggle/working/garbage-data/test/images/telechargement_jpg.rf.a25e6b5fa4ad19ab2dc4eb91f9d2bcc6.jpg: 640x640 1 garbage, 6.0ms
image 254/258 /kaggle/working/garbage-data/test/images/telechargement_jpg.rf.d6afa75ac6022b4884ac95b22e54001b.jpg: 640x640 2 garbages, 5.8ms
image 255/258 /kaggle/working/garbage-data/test/images/trash_jpg.rf.aa2dd880d39444842caaad0734634f53.jpg: 640x640 1 garbage, 5.5ms
image 256/258 /kaggle/working/garbage-data/test/images/tunisie_jpg.rf.e94e4deb3ba21378f8f340441a248a13.jpg: 640x640 1 garbage, 5.4ms
image 257/258 /kaggle/working/garbage-data/test/images/unsorted-garbage-dump-heap-of-dirty-waste-picture-id1213755763_jpg.rf.b76384dcebbb5ca286ce7282f3df0136.jpg: 640x640 1 garbage, 5.5ms
image 258/258 /kaggle/working/garbage-data/test/images/via-fimia-e-via-palazotto-2_jpg.rf.fc9bff66549f17d90a7c27594dfdb5ae.jpg: 640x640 1 garbage, 5.4ms
Speed: 1.3ms preprocess, 5.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)
Results saved to runs/detect/predict
💡 Learn more at https://docs.ultralytics.com/modes/predict

Predicted Images¶

In [16]:
!ls /kaggle/working/runs/detect/predict

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In [25]:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Load your image
img = mpimg.imread('/kaggle/working/runs/detect/yolov8n_garbage/results.png')

# Plot the image
plt.figure(figsize=(15, 8))  # you can change the size if you want
plt.imshow(img)
plt.axis('off')  # Hide axis
plt.title('Sample Garbage Detection Output', fontsize=16)
plt.show()
In [24]:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Load your image
img = mpimg.imread('/kaggle/working/runs/detect/yolov8n_garbage/confusion_matrix.png')

# Plot the image
plt.figure(figsize=(15, 8))  # you can change the size if you want
plt.imshow(img)
plt.axis('off')  # Hide axis
plt.title('Sample Garbage Detection Output', fontsize=16)
plt.show()
In [21]:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Load your image
img = mpimg.imread('/kaggle/working/runs/detect/yolov8n_garbage/train_batch9202.jpg')

# Plot the image
plt.figure(figsize=(8, 6))  # you can change the size if you want
plt.imshow(img)
plt.axis('off')  # Hide axis
plt.title('Sample Garbage Detection Output', fontsize=16)
plt.show()
In [36]:
import os
from IPython.display import Image, display

# Get list of predicted images
pred_dir = r'/kaggle/working/runs/detect/predict'
image_files = [f for f in os.listdir(pred_dir) if f.endswith('.jpg')]

# Display up to 5 images (or adjust as needed)
for img_file in image_files[:30]:
    image_path = os.path.join(pred_dir, img_file)
    print(f"Displaying: {img_file}")
    display(Image(filename=image_path))

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In [ ]:

Egpt Waste¶

In [37]:
!yolo task=detect mode=predict model=/kaggle/working/runs/detect/yolov8n_garbage/weights/best.pt source=/kaggle/input/local-waste conf=0.25 save=True

Ultralytics 8.3.117 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (Tesla P100-PCIE-16GB, 16269MiB)
Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs

image 1/4 /kaggle/input/local-waste/IMG-20250426-WA0007.jpg: 640x384 2 garbages, 44.6ms
image 2/4 /kaggle/input/local-waste/IMG-20250426-WA0008.jpg: 640x384 1 garbage, 6.4ms
image 3/4 /kaggle/input/local-waste/IMG-20250426-WA0009.jpg: 640x640 4 garbages, 6.1ms
image 4/4 /kaggle/input/local-waste/IMG-20250426-WA0010.jpg: 640x480 2 garbages, 32.4ms
Speed: 3.0ms preprocess, 22.4ms inference, 46.7ms postprocess per image at shape (1, 3, 640, 480)
Results saved to runs/detect/predict2
💡 Learn more at https://docs.ultralytics.com/modes/predict
In [38]:
!ls /kaggle/working/runs/detect/predict2

IMG-20250426-WA0007.jpg  IMG-20250426-WA0009.jpg
IMG-20250426-WA0008.jpg  IMG-20250426-WA0010.jpg
In [41]:
import os
from IPython.display import Image, display

# Directory containing the predicted images
pred_dir = '/kaggle/working/runs/detect/predict2'
image_files = [f for f in os.listdir(pred_dir) if f.endswith('.jpg')]

# Display all images
for img_file in image_files:
    image_path = os.path.join(pred_dir, img_file)
    print(f"Displaying: {img_file}")
    display(Image(filename=image_path))

Displaying: IMG-20250426-WA0008.jpg

Displaying: IMG-20250426-WA0009.jpg

Displaying: IMG-20250426-WA0007.jpg

Displaying: IMG-20250426-WA0010.jpg

Download Tuned Model¶

In [33]:
# Compress the runs directory
!zip -r /kaggle/working/runs.zip /kaggle/working/runs

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  adding: kaggle/working/runs/detect/predict/19aba664-679b-11e5-af8c-40f2e96c8ad8_jpg.rf.e849d6f7fb06fe9c940a99ce24305c46.jpg (deflated 5%)
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  adding: kaggle/working/runs/detect/predict/A-New-York-un-homme-ordures-pour-eveiller-les-consciences_jpg.rf.51ac0f3a7c9958bfacac43cae66ab536.jpg (deflated 5%)
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  adding: kaggle/working/runs/detect/predict/B9724659829Z-1_20200921140003_0002BGGIGN7ETC-1-0_jpg.rf.4d8e7d7195744a031192a5b0dcf5946d.jpg (deflated 5%)
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  adding: kaggle/working/runs/detect/predict/c0a5fb02-6798-11e5-8dd0-40f2e96c8ad8_jpg.rf.ad2d2c9afc781e766fa41a0ed35ecb34.jpg (deflated 5%)
  adding: kaggle/working/runs/detect/predict/huge-garbage-piles-next-to-the-dumpster-after-city-fair-stacks-of-picture-id1175874693_jpg.rf.231082e863e95ff7796b73db9ee2aceb.jpg (deflated 4%)
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  adding: kaggle/working/runs/detect/predict/orduresmarseilleune2_jpg.rf.7c1e084c9055b27330d2b0c4386b4a90.jpg (deflated 4%)
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  adding: kaggle/working/runs/detect/predict/sous-les-poubelles_jpg.rf.b41787ac31894f89cb1f0f78b2602380.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/predict/garbage-collected-in-black-bags-is-in-green-trash-ready-for-disposal-picture-id1294700447_jpg.rf.a819dcad36cbb120ad71ac5226b6716f.jpg (deflated 5%)
  adding: kaggle/working/runs/detect/predict/spazzatura_png.rf.3da74870d8b09675d64624ce8dbdaf92.jpg (deflated 5%)
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  adding: kaggle/working/runs/detect/predict/image33_jpg.rf.8193c8bfaabc0c84e8660cab5c9fa7a1.jpg (deflated 5%)
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  adding: kaggle/working/runs/detect/predict/8b245de2-679e-11e5-b0d3-40f2e96c8ad8_jpg.rf.67050293f3f671f87ba7c9d9fcd9d24a.jpg (deflated 5%)
  adding: kaggle/working/runs/detect/predict/pile-black-garbage-roadside-and-rain-drops-on-the-bag-in-the-city-picture-id960699652_jpg.rf.f68379437119b52531705bf17371cd12.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/predict/41f3a844-943a-11e5-ba6d-40f2e96c8ad8_jpg.rf.16fc49a3e9d38c03163e7097d5233c9b.jpg (deflated 3%)
  adding: kaggle/working/runs/detect/predict/_117865773_1a9c9b4a-d985-4f48-82be-0beeaeaecec8_jpg.rf.5d2ac3aa62369648d48c1f07a83aedb7.jpg (deflated 5%)
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  adding: kaggle/working/runs/detect/predict/unsorted-garbage-dump-heap-of-dirty-waste-picture-id1213755763_jpg.rf.b76384dcebbb5ca286ce7282f3df0136.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/predict/depositphotos_331733326-stock-photo-pile-of-garbage-in-construction_jpg.rf.e25b143516d3d14a0efa9c87bfe5c8a0.jpg (deflated 5%)
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  adding: kaggle/working/runs/detect/predict/images109_jpg.rf.49c899c98b8e479300d9b20b5c2198af.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/predict/c1f1e214-6798-11e5-8dd0-40f2e96c8ad8_jpg.rf.a1ad351901c678fc6e9faf48cefe58a0.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/predict/trash_jpg.rf.aa2dd880d39444842caaad0734634f53.jpg (deflated 6%)
  adding: kaggle/working/runs/detect/predict/depositphotos_421045610-stock-photo-teddy-bear-lies-thrown-away_jpg.rf.6b2226868e43868c04a02d7bf217a723.jpg (deflated 5%)
  adding: kaggle/working/runs/detect/predict/23-de-febrero-bolsa-basura_jpg.rf.6c4a4ea7a8e3cb96675093b0c1c3262e.jpg (deflated 6%)
  adding: kaggle/working/runs/detect/predict/images122_jpg.rf.0b6f98f0f96183631b2a0e0f7d90f4fd.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/predict/atoz_0008_k-is-for-keep-britain-tidy-min_jpg.rf.b10ff58a5a17a080f9aa8324af556eee.jpg (deflated 5%)
  adding: kaggle/working/runs/detect/val/ (stored 0%)
  adding: kaggle/working/runs/detect/val/PR_curve.png (deflated 22%)
  adding: kaggle/working/runs/detect/val/val_batch1_labels.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/val/P_curve.png (deflated 15%)
  adding: kaggle/working/runs/detect/val/R_curve.png (deflated 16%)
  adding: kaggle/working/runs/detect/val/val_batch0_labels.jpg (deflated 5%)
  adding: kaggle/working/runs/detect/val/val_batch1_pred.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/val/val_batch2_labels.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/val/val_batch2_pred.jpg (deflated 4%)
  adding: kaggle/working/runs/detect/val/confusion_matrix_normalized.png (deflated 36%)
  adding: kaggle/working/runs/detect/val/val_batch0_pred.jpg (deflated 5%)
  adding: kaggle/working/runs/detect/val/F1_curve.png (deflated 15%)
  adding: kaggle/working/runs/detect/val/confusion_matrix.png (deflated 38%)
In [34]:
!ls /kaggle/working

garbage-data  runs  runs.zip  yolo11n.pt  yolov8n.pt
In [ ]: